Adverse action systems and methods for communicating adverse action notifications for processing systems using different ensemble modules

ABSTRACT

This invention relates generally to the personal finance and banking field, and more particularly to the field of lending and credit notification methods and systems. Preferred embodiments of the present invention provide systems and methods for automatically generating high quality adverse action notifications based on identifying variations between a declined borrower&#39;s profile and that of approved applicants, both with simple and sophisticated credit scoring systems, using specific algorithms.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 14/954,825, filed 30 Nov. 2018, which is acontinuation application of U.S. patent application Ser. No. 14/169,400,filed Jan. 31, 2014, which claims priority to U.S. ProvisionalApplication No. 61/759,081, all of which are hereby incorporated intheir entirety by reference. This application also relates to U.S.application Ser. No. 13/454,970, filed Apr. 24, 2012, which is alsohereby incorporated in its entirety by reference.

TECHNICAL FIELD

This invention relates generally to the personal finance and bankingfield, and more particularly to the field of lending and creditnotification methods and systems.

BACKGROUND AND SUMMARY

People use credit daily for purchases large and small. Lenders, such asbanks and credit card companies, use “credit scores” to evaluate thepotential risk posed by lending money. These measures purport todetermine the likelihood that a person will pay his or her debts.

But credit scoring systems are a recent innovation. In the 1950's and1960's, credit decisions were made by bank credit officials, who knewthe applicant. Since applicants usually lived in the same town, creditofficers would make subjective decisions based on their knowledge of theapplicant. Then, in the 1970's, the advent of “FICO scores” made creditfar more available, effectively removing the credit officer from theprocess. As the idea of using statistical computations to measure riskcaught on, lenders began using a variety of other credit scoring methodsand models. Examples include simple systems (such as those offered byExperian, Equifax, TransUnion) and advanced models with many traditionaland non-traditional variables, and many thousands of meta-variables,such as those described by the Applicant in U.S. application Ser. No.13/454,970 and its related continuations.

As a result of decreased physical interaction with lenders and increasedcomputational complexity of calculating “creditworthiness,” consumers'visibility into what “drives” their credit scores diminished.

In response, laws such as the Consumer Credit Protection Act and FederalEqual Credit Opportunity Act, were designed, in part, to ensure thatborrowers are not denied loans for discriminatory purposes. They werealso designed, in part, to educate consumers as to the reasons why theywere denied loans.

Federal and some state laws require lenders send “adverse actionletters” to denied applicants explaining the reasons for thosedecisions. Theoretically, the letter should also allow an opportunityfor the borrower to determine (1) if there is an error in his or herrecords which has led to the denial of credit; as well as (2) providethe borrower with information that helps identify which part(s) ofhis/her credit history is problematic.

But lenders engage in only minimal compliance, often providing genericreasons for their decisions. Unfortunately, these adverse action lettersdo very little to actually help consumers verify their credit historyand/or determine which actions could increase their creditworthiness (atleast, insofar as these mathematical models are concerned). This outcomeis not beneficial from a lending policy standpoint as customers aren'tgetting what they need.

However, generating adverse actions letters that provide useful consumerfeedback is not a straightforward task. Describing market factors as areason for denials, in and of itself, is a complex job. But whencompounded with the complexity of the newer mathematical credit scoringmodels, the job of effectively communicating the reasons is far morechallenging. Indeed, pinpointing one or more variables—jointly orseverally—that correlate to increased credit scores involves a complexanalysis that few lenders, if any, would bother to perform, much lesscommunicate to their consumers.

Accordingly, improved systems and methods for generating high qualityadverse action letters would be desirable.

SUMMARY OF THE INVENTION

To improve upon existing systems, preferred embodiments of the presentinvention provide a system and method for automatically generating highquality adverse action notifications. One preferred method forautomatically generating high quality adverse action notifications caninclude entering and/or importing a borrower dataset and a lender'scredit criteria at a first computer (borrower data and lender criteria);processing the dataset variables and/or sets of variables in thelender's algorithms to identify which variables, when changed, result inan increased credit score (field selection); ranking individualvariables and/or sets of variables in the borrower dataset to yield thegreatest differences in a credit score (field ranking); and generating areport showing which variables and/or sets of variables, when changed,result in an acceptable credit score (reason test generation). Asdescribed below, the preferred method can further include formatting thereason set generation into an adverse action letter that isunderstandable and usable by the consumer (adverse action lettergeneration). Other variations, features, and aspects of the system andmethod of the preferred embodiment are described in detail below withreference to the appended drawings.

The present invention could be used independently (by simply generatingadverse action letters) or in the alternative, the present inventioncould also be interfaced with, and used in conjunction with, a systemand method for providing credit to borrowers. An example of such systemsand methods is described in U.S. patent application Ser. No. 13/454,970,entitled “System and Method for Providing Credit to UnderservedBorrowers, to Douglas Merrill et al, which is hereby incorporated byreference in its entirety (“Merrill Application”).

Other systems, methods, features and advantages of the invention will beor will become apparent to one with skill in the art upon examination ofthe following figures and detailed description. It is intended that allsuch additional systems, methods, features and advantages be includedwithin this description, be within the scope of the invention, and beprotected by the accompanying claims.

BRIEF DESCRIPTION OF THE FIGURES

In order to better appreciate how the above-recited and other advantagesand objects of the inventions are obtained, a more particulardescription of the embodiments briefly described above will be renderedby reference to specific embodiments thereof, which are illustrated inthe accompanying drawings. It should be noted that the components in thefigures are not necessarily to scale, emphasis instead being placed uponillustrating the principles of the invention. Moreover, in the figures,like reference numerals designate corresponding parts throughout thedifferent views. However, like parts do not always have like referencenumerals. Moreover, all illustrations are intended to convey concepts,where relative sizes, shapes and other detailed attributes may beillustrated schematically rather than literally or precisely.

FIG. 1 is a diagram of a system for automatically generating highquality adverse action notifications in accordance with a preferredembodiment of the present invention.

FIG. 2 depicts an overall flowchart illustrating an exemplary embodimentof a method by which high quality adverse action notifications areautomatically generated.

FIG. 3 depicts a flowchart illustrating an exemplary embodiment of amethod for important field selection.

FIG. 4 depicts a flowchart illustrating an exemplary embodiment of amethod for finding the path to adequacy.

FIG. 5a depicts a flowchart illustrating an alternative exemplaryembodiment short titled “swapping codes” as contained in the method fordetermining the path to adequacy.

FIG. 5b depicts a flowchart illustrating an alternative exemplaryembodiment short titled “selection by scoring” as contained in themethod for determining the path to adequacy.

FIG. 5c depicts a flowchart illustrating an alternative exemplaryembodiment short titled “mutation” as contained in the method fordetermining the path to adequacy.

FIG. 5d depicts a flowchart illustrating an alternative exemplaryembodiment short titled “cross-over” as contained in the method fordetermining the path to adequacy.

DEFINITIONS

The following definitions are not intended to alter the plain andordinary meaning of the terms below but are instead intended to aid thereader in explaining the inventive concepts below:

As used herein, the term “BORROWER DEVICE” shall generally refer to adesktop computer, laptop computer, notebook computer, tablet computer,mobile device such as a smart phone or personal digital assistant, smartTV, gaming console, streaming video player, or any other, suitablenetworking device having a web browser or stand-alone applicationconfigured to interface with and/or receive any or all data to/from theCENTRAL COMPUTER, USER DEVICE, and/or one or more components of thepreferred system 10.

As used herein, the term “USER DEVICE” shall generally refer to adesktop computer, laptop computer, notebook computer, tablet computer,mobile device such as a smart phone or personal digital assistant, smartTV, gaming console, streaming video player, or any other, suitablenetworking device having a web browser or stand-alone applicationconfigured to interface with and/or receive any or all data to/from theCENTRAL COMPUTER, BORROWER DEVICE, and/or one or more components of thepreferred system 10.

As used herein, the term “CENTRAL COMPUTER” shall generally refer to oneor more sub-components or machines configured for receiving,manipulating, configuring, analyzing, synthesizing, communicating,and/or processing data associated with the borrower and lender. Any ofthe foregoing subcomponents or machines can optionally be integratedinto a single operating unit, or distributed throughout multiplehardware entities through networked or cloud-based resources. Moreover,the central computer may be configured to interface with and/or receiveany or all data to/from the USER DEVICE, BORROWER DEVICE, and/or one ormore components of the preferred system 10 as shown in FIG. 1. TheCENTRAL COMPUTER may also be the same device described in more detail inthe Merrill Application, incorporated by reference in its entirety.

As used herein, the term “BORROWER'S DATA” shall generally refer to theborrower's data in his or her application for lending as entered into bythe borrower, or on the borrower's behalf, in the BORROWER DEVICE, USERDEVICE, or CENTRAL COMPUTER. By way of example, this data may includetraditional credit-related information such as the borrower's socialsecurity number, driver's license number, date of birth, or otherinformation requested by a lender. This data may also includeproprietary information acquired by payment of a fee through privatelyor governmentally owned data stores (including without limitation,through feeds, databases, or files containing data). Alternatively, thisdata may include public information available on the internet, for freeor at a nominal cost, through one or more search strings, automatedcrawls, or scrapes using any suitable searching, crawling, or scrapingprocess, program, or protocol. Moreover, borrower data could includeinformation related to a borrower profile and/or any blogs, posts,tweets, links, friends, likes, connections, followers, followings, pins(collectively a borrower's social graph) on a social network. The listof foregoing examples is not exhaustive.

As used herein, the term “LENDER CRITERIA” shall generally refer to thecriteria by which a lender decides to accept or reject an applicationfor credit as periodically set in the USER DEVICE or CENTRAL COMPUTER.By way of example, these criteria may include accept or reject criterionbased on individual data points in the BORROWER'S DATA (such as lengthof current residence >6 months), or based on complex mathematical modelsthat determine the creditworthiness of a borrower.

As used herein, the term “NETWORK” shall generally refer to any suitablecombination of the global Internet, a wide area network (WAN), a localarea network (LAN), and/or a near field network, as well as any suitablenetworking software, firmware, hardware, routers, modems, cables,transceivers, antennas, and the like. Some or all of the components ofthe preferred system 10 can access the network through wired or wirelessmeans, and using any suitable communication protocol/s, layers,addresses, types of media, application programming interface/s, and/orsupporting communications hardware, firmware, and/or software.

As used herein and in the claims, the singular forms “a,” “an,” and“the” include plural references unless the context clearly dictatesotherwise.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meanings as commonly understood by one of ordinary skillin the art.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention. Although any methods, materials, and devices similar orequivalent to those described herein can be used in the practice ortesting of embodiments, the preferred methods, materials, and devicesare now described.

The present invention relates to improved methods and systems forautomatically generating high quality adverse action notifications,which includes notifications for individuals, and other types ofentities including, but not limited to, corporations, companies, smallbusinesses, and trusts, and any other recognized financial entity.

Prior to describing the preferred system and method, a frame ofreference is in order: As many consumers know, a person's credit historyis made up of a number of variables, such as the amount of debt theperson is presently carrying, their income stability, their repaymenthistory on past debt (lateness or failure to pay), and the length oftheir credit history. However, consumers do not often appreciate thatmodern credit scoring systems have significantly increased insophistication, now containing many variables and meta-variables aswell.

But credit applications are typically underwritten in a fairlystereotyped fashion. First, information is gathered from an applicant,generating an application. This application is supplemented with otherdata relevant to the application, including publicly available data(e.g. “Does the applicant have an active lien filed against him/her?”)or proprietary data (e.g. “How many credit applications has this personfiled in the last 90 days?”) The resulting record is provided to a“scoring system,” which assigns a numerical score to the application.This score is compared to a threshold, and the applicant receives a loanif the score exceeds that threshold.

Given this process, the problem of determining the reasons for adeclined application appears straightforward: simply go through the listof variables (also called “signals”) used in the scoring process, andfind the ones which could make the score change towards the threshold.This appearance can be deceptive.

Modern scoring functions almost never produce monotone base signals.Even scoring systems that use monotone functions such as logisticregression to produce their final outputs use intermediatemeta-variables that transform primary signals into distilled signalsthat are no longer monotone in the values of those primary signals. As aresult, knowing that a small change in a given signal locally increasesthe application's score for a given application gives no assurance thata large change in the same signal will necessarily still increase theapplication's score. In addition, since a change in a single variablemight not have the same effect as a change in several variablestogether, it would not necessarily be enough to look at one-variableperturbations when searching for “better results.”

Because detection of the underlying variables that drive an applicant'sscore has become exceedingly complex, in turn, intelligibly reportingthe right reasons for deficiency in a person's score requires not onlysophisticated analysis, but also a translation into understandablelanguage. Indeed, it is because of this complexity that few lenders, ifany, would bother to perform the analysis, much less communicate totheir consumers. The preferred embodiment of the present inventionsolves this problem through an automated method and process forperforming the analysis and intelligibly communicating the results, asfurther described below:

System:

As shown in FIG. 1, a preferred operating environment for automaticallygenerating high quality adverse action notifications in accordance witha preferred embodiment can generally include data sources (theborrower's application 13, and the lender's credit model 15), a USERDEVICE 30, a CENTRAL COMPUTER 20, a NETWORK 40, and one or morecommunication devices from which the borrower is issued an adverseaction letter, including a BORROWER DEVICE 12, Email Server 30, and/or aPrint Server 40. The preferred system 10 can include at least: datasources (the borrower's application 13, and the lender's credit model15), and a computer to analyze and process the data sources (CENTRALCOMPUTER 20 and/or a USER DEVICE 30), which function to generate highquality adverse action notifications. To be clear, the borrower'sapplication 13 should include one or more variables in the BORROWERDATA, and the lender's credit model 15 should include one or morealgorithms from the LENDER'S CRITERIA. In particular, the preferredsystem 10 functions to helps borrowers determine the accuracy of his/hercredit file as well as provide information to improve his/hercreditworthiness, by accessing, evaluating, measuring, quantifying, andutilizing a the novel and unique methodology described below.

More specifically, this invention relates to the preferred methodologyfor automatically generating high quality adverse action notificationsthat takes place within the CENTRAL COMPUTER 20 and/or a USER DEVICE 30,after gathering and/or downloading the BORROWER'S DATA 13 and the LENDERCRITERIA 15.

Method

FIG. 2 provides a flowchart illustrating one preferred method forautomatically generating high quality adverse action notifications whichinvolves the following steps: (a) gathering the BORROWER DATA 100 for afailed credit application; (b) important field selection 200 (to compareBORROWER DATA against the LENDER CRITERIA 600); (c) field ranking 300;(d) reason text generation 400; and (e) generating adverse actionletters 500.

In the first step, all data from the borrower's failed application(BORROWER DATA 100) is temporarily gathered for collection by a computer(such as the CENTRAL COMPUTER 20 in FIG. 1). For example, the BORROWERDATA 100 may include classic financial data such as the borrower'scurrent salary, length of most recent employment, and the number ofbankruptcies. Additionally, the BORROWER DATA 100 may include otherunique aspects of the borrower, such as the number of organizations theborrower has been or is currently is involved with, the number offriends the borrower has, or other non-traditional aspects of theborrower's identity and history such those identified in the MerrillApplication. Subsets of BORROWER DATA 100 are used to determine theborrower's credit score.

For illustrative purposes, fictitious BORROWER DATA 100 for Ms. “A” (acreditworthy applicant), Mr. “B” (a declined applicant), the averageapproved applicant, and the perfect applicant are shown below:

Avg. Perfect Variable Ms. “A” Mr. “B” Applicant Applicant Loan Requested$400 $7,500 $1,500 <$3,000 Income $32K $65K $48K $100K Rent $800/mo$1,200/mo $1,000/mo $1,000/mo Address 2 addresses 7 addresses in 2addresses 1 addresses Information in 10 years past 5 years in 5 years in5 years Late Payments 1—gas None <2 bills within None bill. 5 yrs.Social Security One (1) Four (4) One (1) One (1) Number registeredregistered registered registered SSN SSN SSN SSN Credit Score 82 75 >80100 (out of 100)

Referring back to FIG. 2, the second step is important field selection200. Important field selection is the creation of a list of BORROWERDATA variables whose values either reduce or increase the application'scredit score by sufficiently perceptible amounts when those variablesare changed, and processed through the LENDER CRITERIA 600.

As shown in FIG. 3, important field selection 200 may be accomplished bydetermining the shortest path between the borrower's credit applicationand the “perfect application” (shortest path 210). Alternatively,important field selection 200 may be accomplished by finding the mostimportant changes between the borrower's application and an “adequateapplication” that is approved for funding (path to adequacy 220). Bothmethods are discussed below:

Starting with the first method, the shortest path 210: as its nameimplies, the shortest path 210 is a protocol in which a list of allfields (variables) are identified where there is difference between theBORROWER DATA and the data of a “perfect” applicant. Given that a“perfect” application (one which receives the highest possible score)will always be funded, one way to build an explanation for why adifferent application was not approved is to find the set of differencesbetween the unfunded application and the perfect application. Thus, as apreliminary step, the preferred method is to record a list of fields onwhich the two applications differ.

By way of example, we again refer to the fictitious credit applicant,Mr. “B” whose shortest path is as follows:

Variable Mr. “B” Perfect Applicant Difference Loan Requested $7,500<$3,000 $4,500 Income $65K $100K $35K Rent $1,200/mo $1,000/mo $200/moAddresses 7 addresses 1 address 6 addresses SSN Four (4) One (1) 3 SSNCredit Score 75 100 25

It is important to remember that BORROWER DATA 100 could include dozensvariables or hundreds of thousands of meta-variables. And depending onthe sophistication of the Lender's credit scoring system, some or mostof those variables and meta-variables may not be used in determining aborrower's credit score. The shortest path 210, may not be helpful tothe applicant in (1) identifying flaws in his credit profile and (2)determining what actions would be necessary to improve hiscreditworthiness. Thus, if an applicant takes selective actions in“remedying” portions of his/her credit profile; those changes may notresult in a score improvement that would meet the LENDER CRITERIA 600.In other words, the borrower may not be able to recognize whichvariables are important, and which are just chaff.

As a result, the preferred method in the shortest path 210 includes anintermediate step that eliminates “low impact” fields (which are lateromitted from the reason text generation 400, and in turn, the adverseaction letter 500 as shown earlier in FIG. 2).

Contrary to logical conclusions, the preferred method for eliminating“low impact” fields does not directly identify “low impact” fields.Rather, the focus is to find the “signals” that are important. And inorder to find the signals that are important, the preferred method is topick the variables which require the smallest transformation (i.e. theshortest path) from a given application to an application with a perfectscore. A singular path may be chosen at random with signals thenselected based on their relative impact. Alternatively, if multiplepaths are available, then lists of variable are ranked by frequency, ifpossible.

Path-finding is a well-studied problem in machine learning in either agraph or a continuous domain, and there are many well-studied algorithmsfor finding optimal or near-optimal paths, including, withoutlimitation: ant colony optimization, swarm-based optimizationtechniques, steepest and stochastic descent algorithms. In addition,there are many multidimensional optimization algorithms available, whichhas been a major area of study in computer science since the firstcomputer was built. Other path finding algorithms may be used as welldepending on suitability to the data set and/or desired outcome. Thus,depending on the nature of the algorithms in the LENDER CRITERIA 600,these path finding algorithms may be applied singularly, or in a hybridapproach, depending on whether the features of the LENDER CRITERIAand/or BORROWER DATA 100 are continuous and/or discrete.

To illustrate the differences between continuous and discrete LENDERCRITERIA and/or BORROWER DATA 100, additional examples may be helpful. Alender criterion might be discrete (e.g., Does the borrower have a joband a checking account?). Similarly, a borrower signal can also bediscrete (e.g., is the borrower employed (yes/no)? Does the borrowerhave a bank account (yes/no)?). Conversely, a lender criterion can becontinuous (weight the application negatively according to the averageamount of ethanol consumed by the applicant each week). And thecorresponding borrower signal would also be continuous (how many beershave you drunk in the last week? Glasses of wine? Mixed drinks/otherdistilled liquor products?)

The shortest path 210 may be further “filtered” whereby denials forseemingly spurious fields (such as the number of friends one has insocial media), could be eliminated from the important field selection200 list.

FIG. 4 provides a second perspective in illustrating the preferredmethod to find the shortest path 210. In order to find the shortest path210, a comparison of known good application(s) 211 would be made againstknown bad application(s) 212. From this comparison, a list of identicalsignals 213 and different signals 214 could be obtained. Thereafter, theincremental changes to the variables/fields that produce differentsignals 214 would be run against a series of selection tests 215. Onetest might determine if changes to individual variables, or sets ofvariables, result in a sufficiently improved credit score. A second testmay eliminate those fields, that when changed, does not result insubstantial improvement—or any improvement—in the applicant's creditscore. A third test may include a manual filter whereby certainvariables/fields are eliminated for administrative purposes.

Referring back to FIG. 3, a second preferred method to important fieldselection 200 may be achieved by finding the most important changes in apath to an adequate application (path to adequacy 220).

Unlike the shortest path method 210, which generally returns one path(or depending on the tests employed in determining the “perfectapplication,” a few paths), the path to adequacy 220 is likely to returnnumerous paths to fundability.

The preferred method for generating the path to adequacy 220 seeks theshortest paths from a given application to applications that have scoresexceeding a specified threshold (where the threshold is no greater thanthe maximum possible value of the scoring function). The methods fordoing so are similar to that found in the shortest path 210, except thatinstead of comparing the borrower's profile to a perfect application, itis instead compared to a collection of accepted applicants.

There are a lot more path(s) to adequacy 200 than path(s) to perfection.Unfortunately, the range of possible “acceptable” applications usuallyis not structured nicely. There are many subtle interactions amongdifferent signals in an application. This means that some set of signalsis likely to occur. Often, however, there are so many sets of changesthat it would be effectively impossible to examine each possibility toreach an acceptable application. Instead, the preferred method of thepresent invention is to identify a set of changes to the failingapplication when compared to previously collected approved applicants.

Depending on the sophistication of the LENDER CRITERIA, the number ofsubsets of the list of exchanged fields grows exponentially in thenumber of fields. It is impossible to enumerate all of them. Instead,the preferred approach is probabilistic: taking random subsets of theset of exchanged fields, and measuring the resulting score change. Insuch instances, the preferred method is to use the score changes overall samples. The result turns out to be a rough weighting of thecontribution of the individual fields to the final score change.

By way of example, we again refer to the fictitious credit applicant,Mr. “B” whose path to adequacy is as follows:

Variable Mr. “B” Sample Applicant Difference Loan Requested $7,500$1,500 $6,000 Income $65K $48K N/A Rent $1,200/mo $1,000/mo $200/moAddresses 7 addresses 2 addresses 5 addresses SSN Four (4) One (1) 3 SSNCredit Score 75 100 25

Referring back to FIG. 2, the third step is field ranking 300. Thepreferred approach for field ranking 300 will depend on whetherimportant field selection 200 is accomplished by way of the shortestpath method 210 or the path to adequacy 220.

If the shortest path method 210 is employed, ranking, although possible,is purely academic. Indeed, and if well specified, the truncation of theshortest path effectively creates an “all or nothing” result of a longlist of fields. In other words, since all changes dictated by theshortest path are necessary to make the application fundable, there isno need to rank the important field selection 200.

If the path to adequacy 220 is employed, the preferred method wouldregulate the number of fields by ranking the fields so thathigher-ranked fields contribute more to a passing score thanlower-ranked ones.

Because more than one shortest path to the threshold is likely to exist,the preferred ranking method would employ a voting strategy. In thepreferred method, the computer performs many simultaneous searches formany paths to the specified threshold, and then the computer votes basedon the number of paths a given field occurs in. Examples include, butare not limited to: membership in the greatest number of paths, changesthat have the greatest impact, or some combination thereof. A completeenumeration of the methods is not possible. However, the preferredmethod will seek to have a meaningful correlation to signal impact, andavoids verging into an arbitrary ranking or scoring function, wherepossible. Notwithstanding, arbitrary ranking or scoring functions are analternative method.

For example, assume that Mr. B's BORROWER DATA has 26 possible paths toadequacy 220. And within those lists, a “loan amount-to-income”meta-variable appears 21 times, the variable “social security numbers”appears in 16 times, and the variable “number of addresses” appears 4times. Let's further assume that one of the protocols in the fieldranking 300 says that fields that appear in at least 20% of the totalpaths (or 5 occurrences) should be reported. In this example, “loanamount-to-income” and “number of social security numbers” would behighest, and in this order.

An alternate method to field ranking 300 is to estimate the“contribution” of each field in each path to the final score difference.As stated above, one method to do so is to take random samples of thefields for any given path and compute the score that arose from justusing values in those fields, and take the average difference across allpaths containing each field as an importance score (while ranking fieldsaccording to their importance).

However, the preferred method for identifying “contributions,” (alsoknown as creating “weighted importance scores”) is either accomplishedby using (1) a ranking by scoring methodology, or (2) through a geneticalgorithm.

In the instant invention, either electronic method can be used to moreefficiently select the most regularly occurring sets of high-impactchanges that could be made within a set of paths (or aggregated portionsof paths) that result in credit approval.

The ranking by scoring method significantly reduces the number ofsearches for adequate paths (or portions thereof) that would lead to anacceptable credit score. Rather than using a purely random selection ofvariables, the ranking by scoring groups items into small sets to beevaluated tournament style. Thus, by limiting the number of sets thatmay be grouped, ranking by scoring effectively ranks a limited, yetdecreasingly random population of paths, which is thereafter ranked.

A simple example may provide a helpful background: As shown in FIG. 5a(single associated exchange score), occurs when the values of one set ofdeficient variables (ID 301), has their values replaced (exchange list303) which results in a new, and preferably acceptable, resulting creditscore (score 302).

As shown in FIG. 5b , when multiple fields are deficient, ranking may bemade by “ranking by scoring.” In essence, ranking importance scores isaccomplished by replacing the values in an initial set of variables(original selection 310) with a second set of values (revised ranking byscoring 311), and then by scoring the possible replacements. Thisprocess is would likely be given a limited universe (e.g., computer,please select 1,000 random sets of variables), then continue exchangingcombinations of variables—tournament style—until the most potent changesare identified and ranked.

In the alternative to ranking by scoring, the use of a genetic algorithmmay be employed. Genetic algorithms are a well-studied area ofcomputational science that seeks to generate useful solutions tooptimization and search problems. In the instant invention, a geneticalgorithm would seek out the “pieces of the paths” that most frequently,and most effectively, produce an acceptable credit score.

At its core, a genetic algorithm uses the evolutionary processes ofcrossover and mutation to randomly assemble new offspring from anexisting population of solutions. The parent solutions are then“selected” to generate offspring in proportion to their fitness. Themore fit, or better matched to the achieving a credit worthy score, anindividual model is, the more often it will contribute its geneticinformation to subsequent generations.

In the present invention, a genetic algorithm would first engage inmutation (randomly identifying sets of variables and changing valueswithin those sets of variables), “cross over” the sets of variables(i.e., find the most effective sets of variables and values to change),and then “select” a population of paths that are more impactful thanothers. This process would be iteratively repeated and optimized through“generations” of changes within the sets of variables to determine howeffectively each set of changes lead to a passing credit score. Duringeach successive generation, a proportion of the existing population isselected to breed a new generation. Individual solutions are selectedthrough a fitness-based process, where fitter solutions (sets of changesthat quantitatively produce greater changes in the credit score) aretypically more likely to be selected.

The number of initially selected sets of variables, and generationswhich traditionally be limited (e.g., computer please pick 1,000 sets ofvariable or computer please limit your search to 100 generations) tolimit processing times.

To illustrate the concept graphically, mutation is illustrated in FIG.5c . In essence, mutation replaces the swap point 320 between oneexchange list and another.

As shown in FIG. 5d , “cross-over” replaces an initial set ofvariables/values (original selection 310) and with a second set ofvariables/values (revised ranking by scoring 311) by mutating possiblereplacements amongst various possibilities.

Generally the average fitness will increase, since only the bestsolutions from the first generation are selected for breeding, alongwith a small proportion of less fit solutions. These less fit solutionsensure diversity within the population of parent solutions and thereforeensure the genetic diversity of the subsequent generation of children.

In other words, mutation and/or cross-over operate to produce a numberof different candidates, which are then ranked by their scores (highestto lowest), and then resampled with a weight according to each score.

An example may be helpful to further explain mutation and cross-over.Consider a system with five input signals: age, employment status, bankaccount status, income, and distance between home and work. The LENDERCRITERIA specifies that only applicants with employment and checkingaccounts are accepted. Moreover, the LENDER CRITERIA will result in arejection if the applicant earns less than $40,000 per year and/or theapplicant lives more than twenty miles from the applicant's place ofemployment. Age is completely ignored. Three hypothetical rejectedapplicants could have the following data:

Applicant C Applicant D Applicant E Age 61 35 37 Employment No, but . .. Yes Yes Checking account No Yes Yes Income $50,000 pension $35,000$30,000 Distance  0 25 27

In the preferred method, the most “important reasons” for each of thethree applicants (C, D, and E) would first look for randomly selectedsets of swaps. Each of those swaps would then be scored. By comparingeach applicant to funded applications, the preferred method wouldgenerate a set of frequencies for each variable (or randomly selectedset thereof). Using random substitution of values for each variable (orsets of variables) would take an inordinate period of time. Therefore,the preferred method could exchange values individually, or in blocks.This resulting set produces an ordering: application C needed a job anda checking account, application D would need to live closer to work, andapplication E would need a checking account and to live closer.

On a broader note, these ranking protocols fall into two categories:continuous parameters and discrete search space. For continuousparameters, algorithms search parameters such as regression and Lyapunovfunctional reduction are particularly well suited. However, for discretesearch space, other suitable search space algorithms, such as purerandom search, simulated annealing, and/or other genetic algorithms arebetter suited.

To recap and further illustrate a practical example of the first threesteps in FIG. 2 (Gathering BORROWER DATA 100, performing important fieldselection 200 by comparing to the LENDER CRITERIA 600, and Field Ranking300), let's take the example of Mr. B and compare (1) his scoredapplication which did not meet the threshold of fundability to (2) otherapplications previously scored that were fundable.

First, the preferred method is to gather Mr. B's BORROWER DATA as wellas extract the subset of previous applications with scores fundabilitythreshold. Next, and assuming the Important Field selection isaccomplished by the path to adequacy 220, the preferred method forimportant field selection 200 is to create an initial population ofexemplars consisting of an index into that subset and a bit vector ofthe same length as the list of features for the LENDER CRITERIA 600.Each exemplar will be scored by taking Mr. B's un-awarded loan andreplacing the list items where the bit vector is 1 with the values fromthe indexed element of the subset. Finally, the preferred method is tocompute the score of Mr. B's modified list. This process will beiteratively repeated until an appropriate termination criterion has beenreached (e.g., all paths to fundability have been identified or themethod-defined maximum number of paths has been identified).

Thereafter, all of the important field selection 200 entries will befield ranked 300. In this step, a new set of exemplars is randomlyselected and weighting according the score. Mr. B's important fieldselection 200 entries are “mutated” to create a subset of thoseexemplars by either replacing the index of the associatedabove-threshold loan or by randomly flipping some number of bits in thebit string. Thereafter, Mr. B's “mutated” important field selection 200entries are “crossed over.” Here, “cross-over” is accomplished by takinga subset of the exemplars by picking pairs of items, and, for each suchpair, selecting a single point in the exemplar's bit string, andexchanging the contents of the bit strings beyond that point. Theprocess is repeated until all possible paths to adequacy are rankedand/or voted according to “contribution” of each field has beencomputed, and sorted from most influential to least influential.

It should be noted that the example of Mr. B is a simple andstraightforward genetic algorithm, wherein the preferred method hasfound that the population converges to a set of exemplars that representchanges to fields/variable that produce significant improvements in Mr.B's creditworthiness (i.e. yielding an acceptable risk profile to issuea loan.).

Referring back to FIG. 2, the fourth and fifth steps are reason textgeneration 400 and generating an adverse action letter 500.

There are two aspects of reporting the results of the search. First isreporting the variables that are likely to be wrong (reason textgeneration 400). Second, reporting the reasons for the adverse action(adverse action letter 500). These are not the same, and solving eachrequires different mechanisms.

Reporting the variables that are likely to be wrong (reason textgeneration 400) is straightforward, given the weighted contributionscomputed in the previous section. The preferred method for reason textgeneration 400 involves recording a list of items with the largestpossible weights.

Credit scoring systems often perform veracity checks with third-partydata sources that supply information on the borrower. And if aborrower's profile is inconsistent with what is self-reported and/or hasvalues outside the “norm” of other borrowers, those fields will beflagged, and often result in a deduction from the borrower's creditscore. Thus, there is a strong probability that important errors willshow up with high ranks. Since the values associated with those errorsand the sources from which the erroneous signals were drawn will belisted, consumers will be able to recognize opportunities forsignificantly improving their scores by correcting errors in creditagency files or in their own application data.

At the end of the search and ranking steps, we have one or more“recipes” for transforming a below-threshold loan into anabove-threshold one. Unfortunately, the contents of those recipes arenot ready to present to an applicant, as they simply are information ofthe form “this signal might be associated with a change in the score foryour application.”

Thus, reporting the intelligible reasons for the adverse action letters500 requires additional steps and procedures. The creation of adverseaction letters 500 may be resolved within the standard boundaries ofwell-studied machine learning paradigms. In essence, the “filtered”field list would then be translated to associated qualitative entries.For example, a variable or meta-variable associated with “number ofaddresses” would have at least one text entry associated with it (socalled “report classes”), such as “your residential address has changedmany times in the past five years, indicating that your employment isunstable.”

Report classes are lender-defined, examples of which include messagesthat are prescriptive (“Establish and maintain a bank account for morethan 2 years” or “Avoid overdrawing your checking account and try toschedule your essential payments so you aren't late with your bills”),descriptive (“Lexis-Nexis reports have multiple social security numbersassociated with your name and address. That could be in error, and, ifso, should be corrected,”), and/or monitory (“One or more of the fieldsin your application exhibits features highly correlated with fraud. Youshould look at items reported on your application and correct any errorstherein.”).

The preferred method generates a labeled set of training exemplars whichconnect the weight pattern for a given application to the report classor classes with which the application is associated. Thereafter, thepreferred embodiment could use standard classification techniques suchas support vector machines, k means, learned vector quantization, or EMto build a labeling function.

Any of the above-described processes and methods may be implemented byany now or hereafter known computing device. For example, the methodsmay be implemented in such a device via computer-readable instructionsembodied in a computer-readable medium such as a computer memory,computer storage device or carrier signal.

The preceding described embodiments of the invention are provided asillustrations and descriptions. They are not intended to limit theinvention to precise form described. In particular, it is contemplatedthat functional implementation of invention described herein may beimplemented equivalently in hardware, software, firmware, and/or otheravailable functional components or building blocks, and that networksmay be wired, wireless, or a combination of wired and wireless. Othervariations and embodiments are possible in light of above teachings, andit is thus intended that the scope of invention not be limited by thisDetailed Description, but rather by Claims following.

What is claimed is:
 1. A system comprising: a first processing systemthat is constructed to generate an application decision by using a firstset of data features of a first ensemble module; a second processingsystem that is constructed to generate an application decision by usinga different, second set of data features of a different, second ensemblemodule; and an adverse action system that is constructed to: receive thefirst ensemble module from the first processing system via the Internet,generate at least one first application result by using the firstensemble module, generate a first adverse action notification based ongenerated first application results, and provide the first adverseaction notification to the first processing system via the Internet, andreceive the second ensemble module from the second processing system viathe Internet, generate at least one second application result by usingthe second ensemble module, generate a second adverse actionnotification based on generated second application results, and providethe second adverse action notification to the second processing systemvia the Internet.
 2. The system of claim 1, wherein the first processingsystem is constructed to: generate a first result by processing firstapplication data for a first application, first public information forthe first application, and first proprietary information for the firstapplication by using the first ensemble module, compare the first resultto a first threshold to generate an adverse decision for the firstapplication in real-time, and responsive to generating the adversedecision for the first application, provide the first ensemble module,and the first application data, the first public information, and thefirst proprietary information to the adverse action system via theInternet.
 3. The system of claim 2, wherein the adverse action system isconstructed to: for each of a plurality of variables of data receivedfrom the first processing system: change a first value of the variableto a second value, and generate a first updated result by processing thesecond value by using the first ensemble module, and wherein the adverseaction system is constructed to generate the first adverse actionnotification by using generated first updated results.
 4. The system ofclaim 3, wherein the adverse action system is constructed to: for eachof a plurality of variables of data received from the second processingsystem: change a first value of the variable to a second value, andgenerate a second updated result by processing the second value by usingthe second ensemble module, and wherein the adverse action system isconstructed to generate the second adverse action notification by usinggenerated second updated results.
 5. A method for generating adverseaction notifications for a plurality of processing systems including afirst processing system that is constructed to generate an applicationdecision by using a first set of data features of a first ensemblemodule, and a second processing system that is constructed to generatean application decision by using a different, second set of datafeatures of a different, second ensemble module, the method comprising:an adverse action system receiving the first ensemble module from thefirst processing system via the Internet; the adverse action systemgenerating at least one first application result by using the firstensemble module; the adverse action system generating a first adverseaction notification based on generated first application results; theadverse action system providing the first adverse action notification tothe first processing system via the Internet; the adverse action systemreceiving the second ensemble module from the second processing systemvia the Internet; the adverse action system generating at least onesecond application result by using the second ensemble module; theadverse action system generating a second adverse action notificationbased on generated second application results; and the adverse actionsystem providing the second adverse action notification to the secondprocessing system via the Internet.
 6. The method of claim 5, furthercomprising: for each of a plurality of variables of data received fromthe first processing system, the adverse action system: changing a firstvalue of the variable to a second value, and generating a first updatedresult by processing the second value by using the first ensemblemodule, wherein the adverse action system is constructed to generate thefirst adverse action notification by using generated first updatedresults.
 7. The method of claim 6, further comprising: for each of aplurality of variables of data received from the second processingsystem, the adverse action system: changing a first value of thevariable to a second value, and generating a second updated result byprocessing the second value by using the second ensemble module, whereinthe adverse action system is constructed to generate the second adverseaction notification by using generated second updated results.
 8. Themethod of claim 7, further comprising: the first processing system:generating a first result by processing first application data for afirst application, first public information for the first application,and first proprietary information for the first application by using thefirst ensemble module, comparing the first result to a first thresholdto generate an adverse decision for the first application in real-time,and responsive to generating the adverse decision for the firstapplication, providing the first ensemble module, and the firstapplication data, the first public information, and the firstproprietary information to the adverse action system via the Internet.9. The method of claim 8, further comprising: the second processingsystem: generating a second result by processing second application datafor a second application, second public information for the secondapplication, and second proprietary information for the secondapplication by using the second ensemble module, comparing the secondresult to a second threshold to generate an adverse decision for thesecond application in real-time, and responsive to generating theadverse decision for the second application, providing the secondensemble module, and the second application data, the second publicinformation, and the second proprietary information to the adverseaction system via the Internet.